The cross-domain activity recognition experimental setup is in the following ways. Since different datasets use different sensors and contain different classes of activities, we need to unify our experimental setup for datasets first, and we choose the common parts of the sensors and four common categories of activities. Specifically, we utilize the accelerometer and gyroscope, and each sensor provides 3-axial data (x-, y- and z-axis). We combine them by \(\alpha = \sqrt{x^2+ y^2 + z^2}\)  following [1]}. Then, according to [1]}, we exploit the sliding window technique to extract features, and 19 features from both time and frequency domains are extracted for a single sensor.
We extract 38 features from one position since two sensors are selected. We choose data from Right Arm, Waist, Front Right Hip, and Right Wrist from these four datasets respectively, and choose four categories, including lying, walking, ascending, descending. In experiments, we perform unsupervised domain adaptation on the target domain.
